Safe and secure transportation of hazardous materials (hazmat) is a challenging issue in terms of optimizing risk to society and simultaneously making the shipment delivery economical. The most important safety concern of hazardous material transportation is accidents causing multiple causalities. The potential risk to society from hazmat transportation has led to the evolution of a new threat from terrorism. Malicious entities can turn hazmat vehicles into weapons causing explosions in high profile locations.

The present research is divided into two parts. First, a neural network model is developed to identify when a hazmat truck deviates from its pre-specified path based on its location in the road network. The model identifies abnormal diversions in hazmat carriers’ paths considering normal diversions arising due to incidents. The second part of this thesis develops a methodology for predicting different paths that could be taken by malicious entities heading towards a target after successfully hijacking a hazmat vehicle. The path prediction methodology and the neural network methodology are implemented on the network between Baltimore, Maryland and Washington, DC.

The trained neural network model classified nodes in the network with a satisfactory performance .The path prediction algorithm was used to calculate the paths to two targets located at the International Dulles Airport and the National Mall in Washington, DC. Based on this research, the neural network methodology is a promising technology for detecting a hijacked vehicle in its initial stages of diversion from its pre-specified path. Possible paths to potential targets are plotted and points of overlap among paths are identified. Overlaps are critical locations where extra security measures can be taken for preventing destruction. Thus, integrating both models gives a comprehensive methodology for detecting the initial diversion and then predicting the possible paths of malicious entities towards targets and could provide an important tool for law enforcement agencies minimizing catastrophic events.